Efficient and robust motion perception systems are important pre-requisites for achieving visually guided flights in future micro air vehicles. As a source of inspiration, the visual neural networks of flying insects such as honeybee and Drosophila provide ideal examples on which to base artificial motion perception models. In this paper, we have used this approach to develop a novel method that solves the fundamental problem of estimating angular velocity for visually guided flights. Compared with previous models, our elementary motion detector (EMD) based model uses a separate texture estimation pathway to effectively decode angular velocity, and demonstrates considerable independence from the spatial frequency and contrast of the gratings. Using the Unity development platform the model is further tested for tunnel centering and terrain following paradigms in order to reproduce the visually guided flight behaviors of honeybees. In a series of controlled trials, the virtual bee utilizes the proposed angular velocity control schemes to accurately navigate through a patterned tunnel, maintaining a suitable distance from the undulating textured terrain. The results are consistent with both neuron spike recordings and behavioral path recordings of real honeybees, thereby demonstrating the model's potential for implementation in micro air vehicles which have only visual sensors.

Insects use visual information to estimate angular velocity of retinal image motion, which determines a variety of flight behaviours including speed regulation, tunnel centring and visual navigation. For angular velocity estimation, honeybees show large spatial-independence against visual stimuli, whereas the previous models have not fulfilled such an ability. To address this issue, we propose a bio-plausible model for estimating the image motion velocity based on behavioural experiments of the honeybee flying through patterned tunnels. The proposed model contains mainly three parts, the texture estimation layer for spatial information extraction, the delay-and-correlate layer for temporal information extraction and the decoding layer for angular velocity estimation. This model produces responses that are largely independent of the spatial frequency in grating experiments. And the model has been implemented in a virtual bee for tunnel centring simulations. The results coincide with both electro-physiological neuron spike and behavioural path recordings, which indicates our proposed method provides a better explanation of the honeybee's image motion detection mechanism guiding the tunnel centring behaviour.

The long-term deployment of autonomous robots co-located with humans in real-world scenarios remains a challenging problem. In this paper, we present the “Lindsey” tour guide robot system in which we attempt to increase the social capability of current state-of-the-art robotic technologies. The robot is currently deployed at a museum displaying local archaeology where it is providing guided tours and information to visitors. The robot is operating autonomously daily, navigating around the museum and engaging with the public, with on-site assistance from roboticists only in cases of hardware/software malfunctions. In a deployment lasting seven months up to now, it has travelled nearly 300km and has delivered more than 2300 guided tours. First, we describe the robot framework and the management interfaces implemented. We then analyse the data collected up to now with the goal of understanding and modelling the visitors' behavior in terms of their engagement with the technology. These data suggest that while short-term engagement is readily gained, continued engagement with the robot tour guide is likely to require more refined and robust socially interactive behaviours. The deployed system presents us with an opportunity to empirically address these issues.

Robotics is a multidisciplinary and highly innovative field. Recently, multiple and often minimally connected sub-communities of child-robot interaction have started to emerge, variously focusing on the design issues, engineering, and applications of robotic platforms and toolkits. Despite increasing public interest in robots, including robots for children, child-robot interaction research remains highly fragmented and lacks regular cross-disciplinary venues for discussion and dissemination. This workshop will bring together researchers with diverse scientific backgrounds. It will serve as a venue in which to reflect on the current circumstances in which child-robot research is conducted, articulate emerging and "near future" challenges, and discuss actions and tools with which to meet those challenges and consolidate the field.

Human-Robot Interaction (HRI) research requires the integration and cooperation of multiple disciplines, technical and social, in order to make progress. In many cases using different motivations, each of these disciplines bring with them different assumptions and methodologies. We assess recent trends in the field of HRI by examining publications in the HRI conference over the past three years (over 100 full papers), and characterise them according to 14 categories. We focus primarily on aspects of methodology. From this, a series of practical recommendations based on rigorous guidelines from other research fields that have not yet become common practice in HRI are proposed. Furthermore, we explore the primary implications of the observed recent trends for the field more generally, in terms of both methodology and research directions. We propose that the interdisciplinary nature of HRI must be maintained, but that a common methodological approach provides a much needed frame of reference to facilitate rigorous future progress.

Social HRI requires robots able to use appropriate, adaptive and contingent behaviours to form and maintain engaging social interactions with people. Cognitive Architectures emphasise a generality of mechanism and application, making them an ideal basis for such technical developments. Following the successful first workshop on Cognitive Architectures for HRI at the 2014 HRI conference, this second edition of the workshop focusses specifically on applications to social interaction. The full-day workshop is centred on participant contributions, and structured around a set of questions to provide a common basis of comparison between different assumptions, approaches, mechanisms, and architectures. These contributions will be used to support extensive and structured discussions, with the aim of facilitating the development and application of cognitive architectures to social HRI systems. By attending, we envisage that participants will gain insight into how the consideration of cognitive architectures complements the development of autonomous social robots.

An increasing amount of research is being conducted to determine how a robot tutor should behave socially in educational interactions with children. Both human-human and human-robot interaction literature predicts an increase in learning with increased social availability of a tutor, where social availability has verbal and nonverbal components. Prior work has shown that greater availability in the nonverbal behaviour of a robot tutor has a positive impact on child learning. This paper presents a study with 67 children to explore how social aspects of a tutor robot's speech influences their perception of the robot and their language learning in an interaction. Children perceive the difference in social behaviour between `low' and `high' verbal availability conditions, and improve significantly between a pre- and a post-test in both conditions. A longer-term retention test taken the following week showed that the children had retained almost all of the information they had learnt. However, learning was not affected by which of the robot behaviours they had been exposed to. It is suggested that in this short-term interaction context, additional effort in developing social aspects of a robot's verbal behaviour may not return the desired positive impact on learning gains.

The field of Human-Robot Interaction (HRI) is increasingly exploring the use of social robots for educating children. Commonly, non-academic audiences will ask how robots compare to humans in terms of learning outcomes. This question is also interesting for social roboticists as humans are often assumed to be an upper benchmark for social behaviour, which influences learning. This paper presents a study in which learning gains of children are compared when taught the same mathematics material by a robot tutor and a non-expert human tutor. Significant learning occurs in both conditions, but the children improve more with the human tutor. This difference is not statistically significant, but the effect sizes fall in line with findings from other literature showing that humans outperform technology for tutoring. We discuss these findings in the context of applying social robots in child education.

Subjective appreciation and performance evaluation of a robot by users are two important dimensions for Human-Robot Interaction, especially as increasing numbers of people become involved with robots. As roboticists we have to carefully design robots to make the interaction as smooth and enjoyable as possible for the users, while maintaining good performance in the task assigned to the robot. In this paper, we examine the impact of providing a robot with learning capabilities on how users report the quality of the interaction in relation to objective performance. We show that humans tend to prefer interacting with a learning robot and will rate its capabilities higher even if the actual performance in the task was lower. We suggest that adding learning to a robot could reduce the apparent load felt by a user for a new task and improve the user's evaluation of the system, thus facilitating the integration of such robots into existing work flows.

The role of robot social behaviour in changing people's behaviour is an interesting and yet still open question, with the general assumption that social behaviour is beneficial. In this study, we examine the effect of socially contingent robot behaviours on a charity collection task. Manipulating only behavioural cues (maintaining the same verbal content), we show that when the robot exhibits contingent behaviours consistent with those observable in humans, this results in a 32% increase in money collected over a non-reactive robot. These results suggest that apparent social agency on the part of the robot, even when subtle behavioural cues are used, can result in behavioural change on the part of the interacting human.

Social robots have the potential to provide support in a number of practical domains, such as learning and behaviour change. This potential is particularly relevant for children, who have proven receptive to interactions with social robots. To reach learning and therapeutic goals, a number of issues need to be investigated, notably the design of an effective child-robot interaction (cHRI) to ensure the child remains engaged in the relationship and that educational goals are met. Typically, current cHRI research experiments focus on a single type of interaction activity (e.g. a game). However, these can suffer from a lack of adaptation to the child, or from an increasingly repetitive nature of the activity and interaction. In this paper, we motivate and propose a practicable solution to this issue: an adaptive robot able to switch between multiple activities within single interactions. We describe a system that embodies this idea, and present a case study in which diabetic children collaboratively learn with the robot about various aspects of managing their condition. We demonstrate the ability of our system to induce a varied interaction and show the potential of this approach both as an educational tool and as a research method for long-term cHRI.

Social robots are finding increasing application in the domain of education, particularly for children, to support and augment learning opportunities. With an implicit assumption that social and adaptive behaviour is desirable, it is therefore of interest to determine precisely how these aspects of behaviour may be exploited in robots to support children in their learning. In this paper, we explore this issue by evaluating the effect of a social robot tutoring strategy with children learning about prime numbers. It is shown that the tutoring strategy itself leads to improvement, but that the presence of a robot employing this strategy amplifies this effect, resulting in significant learning. However, it was also found that children interacting with a robot using social and adaptive behaviours in addition to the teaching strategy did not learn a significant amount. These results indicate that while the presence of a physical robot leads to improved learning, caution is required when applying social behaviour to a robot in a tutoring context.

In this contribution, we describe a method of analysing and interpreting the direction and timing of a human's gaze over time towards a robot whilst interacting. Based on annotated video recordings of the interactions, this post-hoc analysis can be used to determine how this gaze behaviour changes over the course of an interaction, following from the observation that humans change their behaviour towards the robot on the time-scale of individual interactions. We posit that given these circumstances, this measure may be used as a proxy (among others) for engagement in the interaction or the human's attribution of social agency to the robot. Application of this method to a sample of unstructured child-robot interactions demonstrates its use, and justifies its utilisation in future studies.

Developments in autonomous agents for Human-Robot Interaction (HRI), particularly social, are gathering pace. The typical approach to such efforts is to start with an application to a specific interaction context (problem, task, or aspect of interaction) and then try to generalise to different contexts. Alternatively however, the application of Cognitive Architectures emphasises generality across contexts in the first instance. While not the "silver-bullet" solution, this perspective has a number of advantages both in terms of the functionality of the resulting systems, and indeed in the process of applying these ideas. Centred on invited talks to present a range of perspectives, this workshop provides a forum to introduce and discuss the application (both existing and potential) of Cognitive Architectures to HRI, particularly in the social domain. Participants will gain insight into how such a consideration of Cognitive Architectures complements the development of autonomous social robots.

A field study was conducted in which CRI activities developed by the ALIZ-E project were tested with the project's primary user group: children with diabetes. This field study resulted in new insights in the modalities and roles a robot aimed at CRI in a healthcare setting might utilise, while in addition (re-)assessed some practises and technologies established within the project. Furthermore, it served as a means of strengthening the bonds with the project's principal stakeholders. The study illustrates on the one hand the feasibility of the activities that were developed within the project, while on the other hand highlights the importance of engaging with primary users in an ongoing, incremental fashion.

Compliance studies in human-robot interaction (HRI) tend to consist of direct requests from the robot to the human. It is suggested that indirect requests are considered more polite, which has been positively correlated with learning gains. An experiment is conducted to explore compliance with indirect robot requests in teaching interactions. A comparison is made across embodiment conditions, but no significant differences are found. Overall, children comply with the robot's requests, which is used to support the hypothesis that given a well-defined context, children will infer the indirect meaning of a suggestion from a robot.

No abstract available.

Being a child with diabetes is challenging: apart from the emotional difficulties of dealing with the disease, there are multiple physical aspects that need to be dealt with on a daily basis. Furthermore, as the children grow older, it becomes necessary to self-manage their condition without the explicit supervision of parents or carers. This process requires that the children overcome a steep learning curve. Previous work hypothesized that a robot could provide a supporting role in this process. In this paper, we characterise this potential support in greater detail through a structured collection of perspectives from all stakeholders, namely the diabetic children, their siblings and parents, and the healthcare professionals involved in their diabetes education and care. A series of brain-storming sessions were conducted with 22 families with a diabetic child (32 children and 38 adults in total) to explore areas in which they expected that a robot could provide support and/or assistance. These perspectives were then reviewed, validated and extended by healthcare professionals to provide a medical grounding. The results of these analyses suggested a number of specific functions that a companion robot could fulfil to support diabetic children in their daily lives.

When studying social interactions, robust data collection protocols can come at the expense of allowing a natural interaction to take place because of a rigid structure in the experimental scenario. This work seeks to explore the use of an interaction mediator as a tool to constrain the content of a social interaction without imposing an interaction structure. Two studies were conducted in different interaction contexts using children: a peer-peer interaction, and a teacher-child interaction. Given that no interaction structure is imposed, objective metrics to characterise behaviour prove difficult to apply. Qualitative analysis techniques, namely Conversation Analysis, are therefore used to study the dyadic interactions. Results confirm the role of the mediating device in providing interaction content without imposing interaction structure. This illustrates the potential role of such devices in manipulating social interactions to facilitate empirical interrogation.

In the development of companion robots capable of any-depth, long-term interaction, social scenarios enable exploration of the robot's capacity to engage a human interactant. These scenarios are typically constrained to structured task-based interactions, to enable the quantification of results for the comparison of differing experimental conditions. This paper introduces a hardware setup to facilitate and mediate human-robot social interaction, simplifying the robot control task while enabling an equalised degree of environmental manipulation for the human and robot, but without implicitly imposing an a priori interaction structure.

We present insights gleaned from a series of child-robot interaction experiments carried out in a hospital paediatric department. Our aim here is to share good practice in experimental design and lessons learned about the implementation of systems for social HRI with child users towards application in "the wild", rather than in tightly controlled and constrained laboratory environments: a trade-off between the structures imposed by experimental design and the desire for removal of such constraints that inhibit interaction depth, and hence engagement, requires a careful balance.